import numpy as np

from statsmodels.tools.tools import Bunch


mpg_bs_poisson = Bunch()

mpg_bs_poisson.smooth0 = Bunch()
mpg_bs_poisson.smooth0.term = 'weight'
mpg_bs_poisson.smooth0.bs_dim = 12
mpg_bs_poisson.smooth0.dim = 1
mpg_bs_poisson.smooth0.p_order = np.array([
    3, 2
    ])
mpg_bs_poisson.smooth0.by = 'NA'
mpg_bs_poisson.smooth0.label = 's(weight)'
mpg_bs_poisson.smooth0.sp = -1
mpg_bs_poisson.smooth0.m = np.array([
    3, 2
    ])
mpg_bs_poisson.smooth0.knots = np.array([
    1488, 1488, 1488, 1488, 1953.22222222, 2118.77777778, 2275,
    2383.88888889, 2515.55555556, 2757.33333333, 3016.44444444,
    3208.11111111, 4066, 4066, 4066, 4066
    ])
mpg_bs_poisson.smooth0.rank = 10
mpg_bs_poisson.smooth0.null_space_dim = 1
mpg_bs_poisson.smooth0.df = 11
mpg_bs_poisson.smooth0.S_scale = 2.44395544177397e-06
mpg_bs_poisson.smooth0.vn = 'weight'
mpg_bs_poisson.smooth0.first_para = 5
mpg_bs_poisson.smooth0.last_para = 15
mpg_bs_poisson.smooth0.S = np.array([
    0.130569121544375, -0.0191732921244136, 0.0202548028417763,
    0.0393112950002946, 0.032061392663319, 0.0241651485426007,
    0.0238771778381105, 0.0389406835250852, 0.0171569521441248,
    0.00680570834700402, 0.00541920498010491, -0.0191732921244136,
    0.0631473273216884, -0.0592997810399249, 0.0178641542128365,
    0.00461438941034586, 0.00793912465273829, 0.00408821971418319,
    -0.000681869049168392, 0.00213834646721613, 0.000935846814259467,
    0.000163463753965317, 0.0202548028417763, -0.0592997810399249,
    0.201022514824508, -0.186241322892132, 0.00284878067469136,
    0.0287314367063779, 0.00263260805442764, -0.00708846456492193,
    0.00065385484055723, 0.000395067932117418, -0.000586348849229599,
    0.0393112950002947, 0.0178641542128365, -0.186241322892132,
    0.479322071007576, -0.286380917395105, 0.0563704638942206,
    0.0348887744557148, 0.0255203052273876, 0.0138979985789615,
    0.00560832050247454, 0.00383270204450356, 0.032061392663319,
    0.00461438941034587, 0.00284878067469137, -0.286380917395105,
    0.414581586658592, -0.181289913269406, -0.00412646444222532,
    -0.00354719180127285, -0.00206283566879658, -0.000685858743449883,
    -0.00142523589453221, 0.0241651485426007, 0.00793912465273829,
    0.0287314367063779, 0.0563704638942206, -0.181289913269406,
    0.213321319885371, -0.0483506678191679, 0.0110304252072989,
    0.0122299348640366, 0.00357726593043956, 0.00234383478372281,
    0.0238771778381105, 0.00408821971418319, 0.00263260805442764,
    0.0348887744557148, -0.00412646444222533, -0.0483506678191679,
    0.0999220996092033, -0.0356092452156359, 0.00921547832469341,
    0.00389860712399989, 0.00147745030481206, 0.0389406835250852,
    -0.000681869049168392, -0.00708846456492192, 0.0255203052273876,
    -0.00354719180127286, 0.0110304252072989, -0.0356092452156359,
    0.0574427000604467, -0.0102653399490586, -0.000311188772793241,
    0.00285258581655637, 0.0171569521441248, 0.00213834646721613,
    0.000653854840557233, 0.0138979985789615, -0.00206283566879658,
    0.0122299348640366, 0.00921547832469341, -0.0102653399490586,
    0.0189789273643945, -0.0062453384832703, 0.00255839876648785,
    0.00680570834700402, 0.000935846814259467, 0.000395067932117419,
    0.00560832050247454, -0.000685858743449884, 0.00357726593043956,
    0.00389860712399989, -0.000311188772793241, -0.0062453384832703,
    0.0211496391608495, -0.0111654555301857, 0.00541920498010491,
    0.000163463753965317, -0.000586348849229599, 0.00383270204450356,
    -0.00142523589453221, 0.00234383478372281, 0.00147745030481206,
    0.00285258581655637, 0.00255839876648785, -0.0111654555301857,
    0.00794139033444708
    ]).reshape(11, 11, order='F')

mpg_bs_poisson.coefficients = np.array([
    3.38155196812657, -0.2398057983025, 0.0385574655971876,
    0.0308789097162079, 0.322087207070371, 0.114222640046661,
    -0.0703319473834318, -0.0932993942273026, -0.164529819124519,
    -0.150860338924587, -0.19827414261489, -0.318132823476157,
    -0.277920321049033, -0.291752287584618, -0.335850782440607,
    0.147266917321495, 0.0655746640077953, 0.00329634588440575,
    -0.0178203244735351, -0.0949069908870388, -0.162736107333124,
    -0.267760412623755, -0.31426029441775, -0.36537967845964
    ])
mpg_bs_poisson.residuals = np.array([
    -0.0597164611104715, -0.0597164611104715, 0.0070681780740447,
    -0.0244130279937029, -0.109459035981563, -0.167042459441988,
    -0.105640645333752, -0.0887544418878843, -0.0954712805695528,
    -0.0784623437350325, -0.0430169241934606, -0.0430169241934606,
    0.0030916078609633, 0.0137773951290736, 0.0148539285398903,
    -0.0370396919392229, -0.0112736908607727, -0.052100214301857,
    0.05696516521011, 0.132586599766586, 0.152452919091744,
    0.0958117450422904, -0.0818874568564589, -0.0996285128088896,
    -0.039947957328768, -0.0297876572186078, -0.0297876572186078,
    -0.0757627215899457, -0.0131089324697808, -0.0233738731200743,
    0.286760172557162, -0.0814957361302606, 0.0714671532989226,
    -0.0558415218265038, -0.0484787948859533, -0.0236434176492324,
    -0.0172259149323173, 0.00318143181414277, 0.0231117802042631,
    0.0285924230117791, 0.0524599141234474, 0.0116354361716793,
    -0.00558296033674166, -0.0940825558878515, 0.132586599766586,
    0.152452919091744, 0.0461585732431811, 0.036440559843717,
    0.036440559843717, 0.00758219423801565, -0.105282911203192,
    -0.0708600665943635, -0.0685585970304921, -0.0501105407364437,
    -0.0478013558335348, -0.295992218769999, -0.295992218769999,
    -0.294876506179699, -0.243930144705301, 0.010594364536701,
    0.0184495378782166, 0.010594364536701, 0.0184495378782166,
    0.0421491667369525, 0.0230515193544041, -0.102317831424696,
    -0.00921877268202032, -0.04273337969419, -0.00119636037327275,
    -0.0461713804461907, 0.00242207736643074, -0.00206774027200307,
    -0.0119510982296829, -0.0463162166023352, -0.0776191103317818,
    0.0686101070886802, 0.118868895618542, -0.0505723217649421,
    -0.0228687782317891, -0.0931020215865397, -0.0139373055232768,
    -0.0329425423946335, -0.019547742575587, -0.00462341430485536,
    -0.0037863985625175, -0.0209367502247506, -0.00850079663664394,
    -0.00363892191661508, -0.00363892191661508, -0.0725184022338961,
    0.0701390830766382, -0.0591199977863917, -0.0498592974299408,
    -0.0100344019333893, -0.0438341236186641, -0.00819025184245747,
    -0.0345607099570335, -0.00404629308005688, -0.017423468562916,
    0.0751572654869439, 0.0669394971058765, -0.0679255314192151,
    -0.0349740550587878, 0.0355822453431487, 0.0642764024494471,
    0.0402581086152893, 0.0766500282298974, -0.108739714671304,
    0.0579475740333115, -0.0762240417848723, -0.0158254364629684,
    -0.106391902749726, 0.0681088212932508, -0.073453881324304,
    -0.00607566275315818, -0.100457295597509, 0.0681088212932508,
    -0.0228550021478378, 0.118868895618542, -0.0996285128088896,
    -0.039947957328768, -0.0297876572186078, 0.0607856968972396,
    -0.0131089324697808, -0.0146135261048062, -0.0264261656715257,
    -0.0145337592605999, -0.0145337592605999, -0.0064905487190714,
    0.226809817977968, -0.0459953733057028, -0.0386603873101858,
    -0.0363408944476897, -0.0267731783454677, 0.00999638170999839,
    0.0169931451495056, 0.00192444906825173, -0.120308776123272,
    -0.0400164322133238, 0.130247697154032, 0.00746981127430812,
    0.0306709674011021, -0.0372819368771609, 0.0976557161110208,
    0.0465873287478877, 0.0269883944212412, -0.0674146853454123,
    0.0873341779571093, 0.0693951026504345, -0.0280641297469413,
    -0.0392924504624044, 0.0735437039091389, -0.0247046596041458,
    0.171463552666685, -0.013091147087648, -0.000813946803385424,
    -0.0956269920449824, 0.0107698324203134, 0.25732769645148,
    -0.0621561621420588, -0.0549179845713752, -0.00160856625504557,
    0.0126062678583092, 0.0705449231002744, 0.0842095493097967,
    0.0883081440038147, 0.0872170109597095, 0.0912812221820642,
    0.123175492931149, 0.131142517763956, 0.184711504210098,
    0.136459914352958, -0.0929831907336212, 0.0885449049874016,
    0.0885449049874016, 0.102638885670704, 0.104987186006888,
    0.0567660298014965, 0.121868643941937, 0.0694291967778775,
    -0.0353205301526058, -0.0107460163888975, -0.0342456641975604,
    -0.00957686458378415, 0.0143852426645283, 0.0450346063721021,
    0.0367753899157743, -0.0896986969406739, -0.101104390140708,
    -0.136305098940538, 0.00460226286555798, 0.0351388253204785,
    0.116934978569832, 0.13995662419881, 0.170013018072364,
    0.191118649321067, -0.0478214884195327, -0.0295329898984451,
    0.124455798890416, 0.0603367500448151, -0.0610962381564639,
    0.0212222309861203, -0.0538512733755478
    ])
mpg_bs_poisson.fitted_values = np.array([
    22.3336888624052, 22.3336888624052, 18.8666471780851, 24.6005745142783,
    20.2124334840001, 22.8102863289666, 21.2442570213741, 20.8505817458946,
    18.7943175653995, 17.3622856225417, 24.0338628565774, 24.0338628565774,
    20.9352763351109, 20.7146066788422, 19.7072696252699, 16.6154304243557,
    16.1824357783393, 15.8244576339389, 44.4669337713292, 33.5515182749217,
    32.9731474236258, 33.764923735666, 33.764923735666, 26.6556641802072,
    32.2899161942785, 31.9517683223134, 31.9517683223134, 25.9673576911837,
    24.3187934206985, 19.4547324478203, 38.080134157885, 33.7505237802536,
    35.4653895670086, 31.7743267613674, 31.5284618343364, 30.7264789753035,
    30.5258354446067, 26.9143737550779, 26.3900783105141, 26.2494642153229,
    25.6541837248854, 23.723961361835, 25.1403576194408, 26.4924802541156,
    33.5515182749217, 32.9731474236258, 22.9410728104038, 14.4726099895799,
    14.4726099895799, 12.9021732165794, 33.5301520174866, 33.3641886280506,
    33.2817500931026, 32.6353763563543, 32.5562320319588, 24.1474603736604,
    24.1474603736604, 24.1092519948462, 21.1620657641, 25.7274341836643,
    25.5290017158504, 25.7274341836643, 25.5290017158504, 34.5439992172317,
    25.4141648862486, 21.1656203778165, 31.2884410253879, 22.9821029307089,
    22.0263514540474, 23.0649401255032, 21.9468430481884, 16.0331523948941,
    16.1935304733726, 14.6799182745066, 15.1781115120846, 17.7801050860014,
    33.0691112648595, 32.6512494955146, 31.725523972003, 26.4638366952653,
    23.3250888902206, 25.8516180226821, 19.3788120289629, 19.0882529015196,
    19.0722150074884, 25.5346117891147, 25.2143419936145, 23.0840008767134,
    23.0840008767134, 33.4238437448952, 42.0506088522863, 32.9478785042368,
    32.6267466662015, 31.3142194643355, 32.4211528205977, 31.2559944662652,
    32.1097352466569, 31.1259446946281, 31.5497052984366, 25.1126052594469,
    25.3060272613759, 18.2388860257967, 17.6161066851271, 18.3471666161138,
    17.8525051915754, 16.3420980420225, 17.6473315393281, 21.3181270530784,
    26.4663398142244, 20.5677576159384, 25.401997700644, 21.2621170941322,
    26.2145573950957, 20.5062647363485, 25.1528200519264, 21.1218432510333,
    26.2145573950957, 18.4210122751131, 33.0691112648595, 26.6556641802072,
    32.2899161942785, 31.9517683223134, 29.2236217839983, 24.3187934206985,
    19.2817747181913, 19.5157258032775, 17.2507177792766, 17.2507177792766,
    17.1110601696662, 13.8570785388883, 22.01247186061, 21.8445175074418,
    21.7919385382283, 21.5777037097056, 18.8119485812727, 18.6825251385611,
    30.9404566669959, 29.5558251512617, 27.0837969236757, 28.3123779686312,
    27.7923960466705, 25.2262854221658, 24.92941694908, 21.8647793180832,
    26.7536202960708, 24.343020949218, 24.662622967121, 21.1526506443608,
    32.7287827606976, 31.8951084621752, 32.2678842431506, 28.8763278915599,
    27.6839218662126, 23.048092224925, 30.3979439554833, 30.0244382955741,
    37.5951069978098, 37.5951069978098, 30.2228290263917, 29.8557167725841,
    29.6270583323936, 29.0467235793694, 28.6389694795548, 24.2866968391243,
    23.9806041337226, 22.052577785282, 22.0747097939672, 21.9924979117765,
    21.3679875950349, 21.2174855273261, 20.2580965194576, 25.5178380106008,
    33.0754619909028, 24.8037539620954, 24.8037539620954, 24.4867112441592,
    18.0997574028658, 17.9793818727964, 17.8273990524644, 17.7664870729599,
    38.3547086431239, 27.2932941866366, 38.3120205919209, 27.2610756297141,
    26.6171064644809, 35.4055260700386, 25.0777557539364, 26.3648968966003,
    26.6994295408305, 21.998508937234, 32.8488210905178, 24.1513499334353,
    20.5920670775752, 20.1762062799231, 20.5125922782815, 20.1491262131442,
    17.8537950533904, 17.5173394077775, 20.4543389101606, 17.9188356898853,
    19.171293940347, 25.4596886075362, 20.0814094711999
    ])
mpg_bs_poisson.linear_predictors = np.array([
    3.10609624980832, 3.10609624980832, 2.93739566357681, 3.20276979690456,
    3.00629793406294, 3.12721158899991, 3.05608660102651, 3.03738184933733,
    2.9335545669874, 2.85430036085041, 3.17946378824737, 3.17946378824737,
    3.04143559905492, 3.03083908808588, 2.98098758418526, 2.81033180724024,
    2.78392644284256, 2.76155669470604, 3.79474585164685, 3.51308211648978,
    3.4956935157856, 3.51942250434414, 3.51942250434414, 3.28300166762353,
    3.4747549893589, 3.46422752584326, 3.46422752584326, 3.25684027583847,
    3.19124944323147, 2.96809035398114, 3.63969273297066, 3.51899593661304,
    3.56855727914211, 3.45865862916789, 3.45089068817213, 3.42512479018631,
    3.41857338881492, 3.2926604842363, 3.27298811794221, 3.26764557798449,
    3.24470666649875, 3.1664855653221, 3.2244744281779, 3.27686092872792,
    3.51308211648978, 3.4956935157856, 3.13292887653641, 2.67225789684402,
    2.67225789684402, 2.55739576358026, 3.51244509424318, 3.50748312800355,
    3.50500920150253, 3.48539686431283, 3.48296881060363, 3.18417921405787,
    3.18417921405787, 3.18259566702687, 3.05221022812765, 3.24755790051531,
    3.23981512809367, 3.24755790051531, 3.23981512809367, 3.54223385111242,
    3.23530669126982, 3.05237818503949, 3.44324873302594, 3.13471577957874,
    3.09223952994195, 3.13831372110367, 3.08862330449352, 2.77465860323819,
    2.78461180897998, 2.68648045603917, 2.71985435790984, 2.87808014041708,
    3.49859965227091, 3.48588312437807, 3.45712152952405, 3.27577914799641,
    3.14952955766808, 3.25237319174925, 2.96418030576864, 2.94907311450918,
    2.9482325642825, 3.24003485689474, 3.22741295935692, 3.13913977482799,
    3.13913977482799, 3.50926952986597, 3.73887386555994, 3.4949268735718,
    3.48513240187112, 3.44407229037009, 3.47881107460801, 3.44221118070723,
    3.46915926286833, 3.43804170595891, 3.45156424816504, 3.22336992164832,
    3.23104259903575, 2.90355590963404, 2.86881363611135, 2.90947515473725,
    2.88214384524492, 2.79374448033398, 2.87058458437584, 3.05955774614591,
    3.27587372992023, 3.02372468537151, 3.23482782056342, 3.0569269490534,
    3.26631488223122, 3.02073043635419, 3.22497001956098, 3.05030773017343,
    3.26631488223122, 2.91349198453504, 3.49859965227091, 3.28300166762353,
    3.4747549893589, 3.46422752584326, 3.37497734744367, 3.19124944323147,
    2.9591603346448, 2.97122059201258, 2.84785375300452, 2.84785375300452,
    2.83972504797555, 2.62879618791377, 3.09160919548466, 3.08394997506595,
    3.0815401095808, 3.07166054590621, 2.93449223078504, 2.92758860238871,
    3.43206460479669, 3.38628085306311, 3.29893564957711, 3.34329909307524,
    3.32476245978116, 3.22788652320362, 3.21604850974011, 3.08487709188384,
    3.2866698023555, 3.1922451944518, 3.20528885733209, 3.05176522361415,
    3.48825489749758, 3.46245265828934, 3.47407243971087, 3.36302215537105,
    3.32085180663105, 3.13758299920276, 3.41437497308165, 3.4020116599001,
    3.62687390889084, 3.62687390889084, 3.40859756683735, 3.39637633817112,
    3.38868807661617, 3.36890569137327, 3.35476835958596, 3.18992874525813,
    3.17724534251483, 3.09342950142183, 3.09443260003746, 3.09070139119307,
    3.06189389526813, 3.05482563075123, 3.00855454176395, 3.23937773743491,
    3.49879167788239, 3.21099501115507, 3.21099501115507, 3.19813057220198,
    2.89589853502537, 2.88922564985387, 2.88073654644261, 2.8773139339888,
    3.64687730106182, 3.30664103782876, 3.64576370051983, 3.30545988365993,
    3.28155410925581, 3.56686791163023, 3.22198122826548, 3.27203346235845,
    3.28464219966599, 3.09097467548117, 3.49191585663117, 3.18434027640117,
    3.02490590830647, 3.00450400319504, 3.0210389550426, 3.00316092334049,
    2.88221609366084, 2.86319121368963, 3.01819503162959, 2.88585243277332,
    2.95341405297199, 3.23709636239996, 2.99979448514897
    ])
mpg_bs_poisson.deviance = 37.5688885982924
mpg_bs_poisson.null_deviance = 337.261844954099
mpg_bs_poisson.iter = 2
mpg_bs_poisson.weights = np.array([
    22.3336888624052, 22.3336888624052, 18.8666471780852, 24.6005745142783,
    20.2124334840002, 22.8102863289666, 21.2442570213741, 20.8505817458947,
    18.7943175653996, 17.3622856225417, 24.0338628565775, 24.0338628565775,
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    29.6270583323937, 29.0467235793695, 28.6389694795549, 24.2866968391243,
    23.9806041337226, 22.0525777852821, 22.0747097939672, 21.9924979117766,
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    19.1712939403471, 25.4596886075362, 20.0814094712
    ])
mpg_bs_poisson.working_weights = np.array([
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    20.2124334840002, 22.8102863289666, 21.2442570213741, 20.8505817458947,
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    26.2145573950958, 20.5062647363486, 25.1528200519265, 21.1218432510334,
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    19.2817747181913, 19.5157258032775, 17.2507177792766, 17.2507177792766,
    17.1110601696662, 13.8570785388883, 22.0124718606101, 21.8445175074419,
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    32.7287827606977, 31.8951084621753, 32.2678842431507, 28.87632789156,
    27.6839218662126, 23.048092224925, 30.3979439554834, 30.0244382955742,
    37.5951069978099, 37.5951069978099, 30.2228290263918, 29.8557167725842,
    29.6270583323937, 29.0467235793695, 28.6389694795549, 24.2866968391243,
    23.9806041337226, 22.0525777852821, 22.0747097939672, 21.9924979117766,
    21.3679875950349, 21.2174855273262, 20.2580965194577, 25.5178380106008,
    33.0754619909028, 24.8037539620955, 24.8037539620955, 24.4867112441593,
    18.0997574028658, 17.9793818727964, 17.8273990524644, 17.7664870729599,
    38.3547086431239, 27.2932941866367, 38.312020591921, 27.2610756297141,
    26.6171064644809, 35.4055260700387, 25.0777557539365, 26.3648968966003,
    26.6994295408305, 21.9985089372341, 32.8488210905178, 24.1513499334354,
    20.5920670775753, 20.1762062799232, 20.5125922782816, 20.1491262131442,
    17.8537950533905, 17.5173394077776, 20.4543389101607, 17.9188356898853,
    19.1712939403471, 25.4596886075362, 20.0814094712
    ])
mpg_bs_poisson.prior_weights = np.array([
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
    ])
mpg_bs_poisson.df_null = 202
mpg_bs_poisson.y = np.array([
    21, 21, 19, 24, 18, 19, 19, 19, 17, 16, 23, 23, 21, 21, 20, 16, 16, 15,
    47, 38, 38, 37, 31, 24, 31, 31, 31, 24, 24, 19, 49, 31, 38, 30, 30, 30,
    30, 27, 27, 27, 27, 24, 25, 24, 38, 38, 24, 15, 15, 13, 30, 31, 31, 31,
    31, 17, 17, 17, 16, 26, 26, 26, 26, 36, 26, 19, 31, 22, 22, 22, 22, 16,
    16, 14, 14, 19, 37, 31, 31, 24, 23, 25, 19, 19, 19, 25, 25, 23, 23, 31,
    45, 31, 31, 31, 31, 31, 31, 31, 31, 27, 27, 17, 17, 19, 19, 17, 19, 19,
    28, 19, 25, 19, 28, 19, 25, 19, 28, 18, 37, 24, 31, 31, 31, 24, 19, 19,
    17, 17, 17, 17, 21, 21, 21, 21, 19, 19, 31, 26, 26, 32, 28, 26, 24, 24,
    28, 25, 23, 23, 35, 31, 31, 31, 27, 27, 30, 30, 34, 38, 38, 28, 28, 29,
    29, 26, 26, 24, 24, 24, 24, 24, 24, 29, 30, 27, 27, 27, 20, 19, 20, 19,
    37, 27, 37, 27, 27, 37, 26, 24, 24, 19, 33, 25, 23, 23, 24, 24, 17, 17,
    23, 19, 18, 26, 19
    ])
mpg_bs_poisson.rV = np.array([
    0, 0, 0, 0, -8.9130297985463e-05, -0.000176684273823698,
    0.0011584583691732, -0.00390049177471793, 0.00344010782379437,
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    -9.86203908496754e-05, -3.64890558944375e-05, -3.08026950210881e-05, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.00732908209018e-08,
    -1.9968429523216e-08, 1.3092616450685e-07, -4.40824151599687e-07,
    3.88792670366615e-07, -1.53467548605631e-07, -1.25884718098402e-08,
    -2.00009544217366e-08, -1.11458381757212e-08, -4.12390489107036e-09,
    -3.48124613097974e-09, 0.000484189713776837, 0.00176326319300196,
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    -9.07732254822129e-06, 6.0169393955401e-07, 2.19117578684648e-06,
    -2.65051351077577e-06, 2.13606089860863e-06, 1.97426328806317e-07,
    5.0207934310591e-07, 2.14133854581071e-07, 6.08574483361064e-08,
    2.27720361659973e-08, 0, 0, 0, 0, -8.67530824448457e-07,
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    0.000783677174543439, -0.000284599371195069, -5.46600275429753e-05,
    -3.4861779513484e-07, 0, 0, 0, 0, 0.000154872794432739,
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    -0.00415592538210646, -0.00148907462126999, 0.00173577250006001,
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    0.00281845357221204, 0.00271275264035418, -0.000438520740299198,
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    0.000316745017030137, 3.28762155221662e-05, -7.65210725613372e-05,
    -0.000234228657698133, -0.000311673452153201, -0.000437967535817843,
    -0.000585886587938954, -0.000192461378311277, 0.000391244883140083,
    0.000693129601873483, 0.00416984196146825, 0.00191987715157721,
    0.000233531125633939, -0.000325479295859366, -0.00247433282397362,
    -0.00446616198281691, -0.00803899886321839, -0.0106982256522182,
    -0.0133066416160238, 0.0144487468289665, 0, 0, -0.039021573780305,
    0.00178251131609767, -0.00314872170017292, -0.00526317798364386,
    -0.00471582757742263, -0.00426509391962734, -0.0021098532647336,
    0.000449733415313781, 0.00250944131155914, 0.00679139580304206,
    0.000266584356546392, -0.00499157509254186, -0.0129502842323007,
    -0.00584017124814522, -0.00038900758484735, 0.00154888351611733,
    0.00854704220691514, 0.0147435174902231, 0.0220373114764713,
    0.0277678791681642, 0.0330732757720991, 0.0673687811308531, 0,
    -0.0727577715645542, -0.0660767215034692, 0.0046318104807724,
    0.00279923327002594, -0.000344618926583256, -0.00145801819850269,
    -0.00309674677991799, -0.0036486478609869, -0.00464834984362868,
    -0.00576615440314964, -0.00207127569768669, 0.00378666514448286,
    0.00764068847822383, 8.88247243745535e-06, 7.70568572451843e-05,
    0.000176716367242848, 0.000255560590323436, 0.000237005166038077,
    4.52872126430998e-05, -0.000545209712186102, -0.00184078849724724,
    -0.00263833664383721, 0.0503224185461385, -0.0469900909623995,
    -0.00485618033033583, -0.0146940851269257, 0.00309766417715181,
    0.00358739721584392, 0.00126780489916931, -4.04115178473884e-05,
    -0.00135981454962773, -0.00215240226396701, -0.00310950655627377,
    -0.004402851468672, -0.00541141005760902, 0.00216272414867472,
    0.00871843645875338, -0.011066944059589, -0.00438326526061835,
    0.000734116197365618, 0.00238488961743991, 0.0079891741761364,
    0.0124369238374935, 0.0172500599680547, 0.018005707666409,
    0.0201741350091917, 0.0401636589575239, -0.0318988645890763,
    -0.01850999070435, -0.00394626508026133, 0.0551382157508856,
    0.0217807180807445, -0.00622899826733438, -0.00923850673879829,
    -0.0212137461370569, -0.020091132050748, -0.0314646112389824,
    -0.0563797107234094, -0.0618781724522286, -0.0809883829460941,
    -0.101451533937108, -0.0294660248157941, -0.0129461041972885,
    -0.000317118828092305, 0.00404893245763359, 0.0193119490827826,
    0.0326887256219942, 0.0524397613334577, 0.0599889895213543,
    0.0707562479561827, -0.00014417166463547, 0.000112486137657318,
    2.93195506570937e-05, 7.11989286865333e-05, 0.000298020443190303,
    7.43500900776159e-05, -0.000123025591619351, -0.000150144864483587,
    -0.000213602220981072, -0.000182287600073906, -0.000196294549615607,
    -0.000273238798774471, -0.000134016463849165, -5.87892574426707e-05,
    -3.61576064062375e-05, 0.00212707138394515, -0.000435955193172209,
    0.000604997293885961, 0.000392867571322303, 0.000561225223337066,
    0.00056952076942879, 0.000186965781647446, 2.43190236830834e-05,
    -1.49153748573756e-05
    ]).reshape(24, 24, order='F')

mpg_bs_poisson.db_drho = np.array([
    -0.00955607829508903, 0.00430986949109399, 0.00462834751450597,
    0.00835107524076425, -0.0333302515945607, 0.00139610358751732,
    0.0263076303556022, 0.0268343416489505, 0.0314648272214792,
    0.0226413860328889, 0.0162437421056309, 0.0181827810783155,
    0.000275213760239914, 0.00115455787426117, 0.00197111543707827,
    0.00493118340740466, 0.00145534674747094, -0.00147150121604032,
    -0.00260852436899397, -0.00515405425200522, -0.00625733949412647,
    -0.00215131919228267, 0.000695879282559118, 0.00245613544165302,
    0.0165502705938555, -0.0120739288987487, -0.00539618833686599,
    -0.00676715282188502, 0.0196760486043603, 0.00772600754507691,
    -0.00364486556122667, -0.00558444320663137, -0.0105223419591228,
    -0.0104699677543541, -0.0139344474571075, -0.0207299030874315,
    -0.0154990384672306, -0.0114599389957355, -0.0101038003601939,
    -0.020380893865003, -0.00665329046642183, 0.00475504810058412,
    0.00904455059277683, 0.0203739259385563, 0.026657106982953,
    0.0214205189927133, -0.0170394753092789, -0.0478344361350309
    ]).reshape(24, 2, order='F')

mpg_bs_poisson.dw_drho = np.array([
    0.198451577496262, 0.198451577496262, 0.0211323116265804,
    0.176795914312368, -0.158842940220042, 0.136552595840399,
    -0.0788956246522186, -0.145800287459428, -0.18078320916676,
    -0.227604968605315, 0.270528113197353, 0.270528113197353,
    0.0871931177612735, 0.0488040223227676, -0.11212062083582,
    -0.0921466191935143, -0.0872523607945422, -0.0726475180150896,
    -2.03674569674128, -0.399858629869372, -0.294145790144752,
    -0.384037673792421, -0.384037673792421, 0.0374542295773345,
    -0.126689730715761, -0.0732225659783105, -0.0732225659783105,
    0.103260641535857, 0.179088091909104, -0.0488091379129944,
    -0.947578113843131, -0.606019988745503, -0.473334587346908,
    -0.236955919521633, -0.195526007765597, -0.0688010780860252,
    -0.0392834493026195, 0.189928469830611, 0.21717147287679,
    0.222511836808396, 0.234040644079886, 0.168755657511205,
    0.169728133790599, 0.366698379522312, -0.399858629869372,
    -0.294145790144752, 0.121380760680706, 0.00910595279608145,
    0.00910595279608145, 0.0340607755348701, -0.340709976973792,
    -0.310583848151182, -0.295779107765152, -0.18359861595662,
    -0.170364313792911, 0.271469840167182, 0.271469840167182,
    0.271257913696808, 0.204605902971062, 0.242930319240712,
    0.240554295492994, 0.242930319240712, 0.240554295492994,
    0.290076687028269, 0.237905640625681, 0.114993524063036,
    0.152699599788878, -0.24062847407969, -0.177222382324359,
    -0.245297972873039, -0.1716897049522, -0.0498413922857943,
    -0.0602427120173084, -0.00606488931871212, -0.0365248804113624,
    -0.0161146042525035, -0.258096696454203, -0.186266616268125,
    -0.0387572286849415, 0.0575914455113098, 0.157609243121204,
    0.220346301053061, -0.0626777547324119, -0.113851702484695,
    -0.116475605634303, 0.22526164753013, 0.223826799079261,
    0.156828878422261, 0.156828878422261, -0.348685874833428,
    -0.0903521318069049, -0.263248163466929, -0.207718489967913,
    -0.00114280263869193, -0.173123855988071, 0.00717472807613134,
    -0.122226134658981, 0.025464920617837, -0.0355698249808467,
    0.187431184982238, 0.181218694001414, -0.170463957448774,
    -0.182265636600245, -0.162639928396832, -0.08984634445544,
    -0.0728162435542022, -0.102375349892814, -0.083518458576145,
    -0.26562651277574, -0.124224763801435, -0.247398017325716,
    -0.0987361747211609, -0.267357057917193, -0.121190412683788,
    -0.235390425297552, -0.102224269330431, -0.267357057917193,
    -0.11647321800326, -0.258096696454203, 0.0374542295773345,
    -0.126689730715761, -0.0732225659783105, 0.25371891049452,
    0.179088091909104, 0.0189353803356997, 0.0439242698170608,
    0.0885475465440703, 0.0885475465440703, 0.0627230661721545,
    -0.0267785017213478, 0.0513972263582553, 0.0260420185827092,
    0.0176322762568806, -0.0186260827562727, -0.0357699577049284,
    -0.0594521157329645, 0.0508444244300699, 0.142346989140971,
    0.125278734192754, 0.126468071771089, 0.17321276456908,
    0.200109710036431, 0.127527720685718, 0.0276890383196247,
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    -0.0443449571558454, -0.0475135961354155, 0.074234409804811,
    0.0216623623678642, 0.352213438519467, 0.21463189015559,
    -0.189097828932044, 0.100405735385079, 0.144407665462729,
    0.336395360175917, 0.336395360175917, 0.12155167143745,
    0.16287496031754, 0.18641467013746, 0.32607579411487,
    0.357175125935867, 0.224343769580642, 0.237299332647382,
    0.196244695452164, 0.198230836612474, 0.190617207491118,
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    0.207342466515434, 0.197169181872743, -0.0572474800374093,
    -0.0728827033179431, -0.10460003726393, -0.107196252644019,
    0.361813401969034, 0.175102284300755, 0.363949986843691,
    0.177442308769387, 0.215769343564575, 0.261298864778967,
    0.172202480804323, 0.184076703337005, 0.163188602303023,
    0.0493811888871374, 0.175827850286979, 0.163383083228185,
    -0.0462305614889876, -0.10500182436349, -0.0595021447805033,
    -0.107718395073629, -0.081368118853961, -0.10288400064567,
    -0.0687391241662543, -0.0840863186508532, -0.0922241572577749,
    -0.279777090829456, -0.113889925777426, 0.0930136922441679,
    0.0930136922441679, 0.0582604661375774, 0.181541045905811,
    0.160535840342632, 0.138401158506572, 0.0244932126510834,
    0.00542933620282285, 0.0633164368215588, 0.128659554552638,
    0.101233497721208, 0.101233497721208, 0.0760249394180194,
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    -0.0831343394498383, -0.0814694950097439, -0.109232145394468,
    0.0770377761224491, 0.041925391053174, 0.0274589171017179,
    0.0274589171017179, 0.347617575580373, -0.0596237774820829,
    -0.0794752848265101, -0.0794752848265101, 0.2925962616859,
    -0.0887185089091997, 0.101775115170702, -0.0645242547362925,
    0.27126368025602, -0.137133999832565, 0.1427646918224,
    0.12693641285656, 0.0754012256843574, 0.0625093748503227,
    0.0612182250236711, 0.022647781585492, 0.0122598524089968,
    -0.031821546967195, 0.0950948808259056, 0.194301250561357,
    -0.176407422753569, 0.0770377761224491, 0.041925391053174,
    -0.162369831909575, -0.0320855187783016, -0.0320855187783016,
    -0.553307949537856, 0.0135199811625741, 0.00368762745876068,
    -0.00119025497296702, -0.0393155997407192, -0.0439714980600599,
    0.110325691275902, 0.110325691275902, 0.107266760517285,
    0.18135826013452, -0.070138627603902, -0.084586374173829,
    -0.070138627603902, -0.084586374173829, -0.18934706121959,
    -0.0929298188222547, 0.0852655356749052, -0.179394458894588,
    0.279166751287957, 0.275139241012056, 0.279948761371457,
    0.275155694583494, 0.0121935242055421, 0.0105151849876446,
    -0.0734774746915535, -0.0830825997308687, -0.0261494773912439,
    -0.0137546788196909, -0.0383815439915174, -0.092751399768813,
    0.332355694092576, 0.245345164899538, 0.0254896012212449,
    0.0970805551002128, 0.0811025946105638, 0.0803177348400819,
    0.00164450882011678, -0.0224311131933797, 0.224672875336535,
    0.224672875336535, 0.0383303142014232, -0.0317090718898804,
    0.0098303983136882, -0.00933161038990583, -0.0873586528394907,
    -0.0215767942260494, -0.0908195449342244, -0.040099049184707,
    -0.0985530505261142, -0.0733677092344781, 0.141756395019668,
    0.157166003405932, 0.0508331038597551, 0.0390688659098563,
    0.0540369357658138, 0.0099459590057849, -0.177557776337536,
    0.00493496128474215, -0.130643486152668, 0.113817128307549,
    -0.142574086775631, 0.100786016919189, -0.15587973038,
    0.109227489252925, -0.163440665982954, 0.099878533303345,
    -0.135287923148148, 0.109227489252925, 0.0333059944181948,
    -0.0137546788196909, 0.347617575580373, -0.0596237774820829,
    -0.0794752848265101, -0.241561147836771, -0.0887185089091997,
    0.0730471534751108, 0.0835002138786142, -0.189280927404009,
    -0.189280927404009, -0.19628855825041, -0.837484070977975,
    0.0753082580083826, 0.0630294638001513, 0.0592929776175618,
    0.0447029449409758, 0.0723308379811164, 0.0645975264797624,
    -0.109593771541039, -0.0811243820180713, -0.0188474302547651,
    0.0685021880400987, 0.0321368032444232, 0.0966486930237446,
    0.0363801672252788, 0.255050941838437, -0.0415198183216256,
    0.0275908041907373, 0.0158480616637525, 0.194606530344896,
    -0.220059902653218, -0.265372977366032, -0.245100901081693,
    -0.434187429432454, -0.274154387736992, -0.498079277975881,
    -0.113234390595495, -0.13592301549537, -0.27298841850198,
    -0.27298841850198, -0.123853386260785, -0.146226945009001,
    -0.160255725328499, -0.220696627341507, -0.245525340689894,
    0.265599932544055, 0.240132044079803, 0.12044536185896,
    0.122242899082191, 0.11558414739394, 0.0672807026649379,
    0.0565215464236424, 0.00320786060661828, 0.0811815737084521,
    0.00442292020833086, 0.0259650355832699, 0.0259650355832699,
    0.00159130624929899, 0.0167237159821922, 0.0123103421759687,
    0.0151464759374235, 0.0138398492897789, -0.378259152221767,
    0.0663775142988527, -0.380910748552257, 0.0640455341462561,
    0.0171809160567121, 0.0133244698759663, 0.189239788364039,
    0.105896755907438, 0.131163117931394, 0.0742692098161327,
    -0.17392768225168, -0.100700445594839, 0.0036126603654632,
    -0.0123831512006035, 5.23650575074128e-05, -0.0132043382182284,
    0.00660287534316686, -0.00167186752805072, -0.00240546718672251,
    0.0118803438893343, 0.0424368089424911, 0.219430516658537,
    -0.0151485147157889
    ]).reshape(203, 2, order='F')

mpg_bs_poisson.aic = 1077.25052939687
mpg_bs_poisson.rank = 24
mpg_bs_poisson.sp = np.array([
    40491.3940640059, 232455.530262537
    ])
mpg_bs_poisson.gcv_ubre = -0.733688545240377
mpg_bs_poisson.scale = 1
mpg_bs_poisson.method = 'UBRE'
mpg_bs_poisson.Vp = np.array([
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    -0.00198704716570621, -0.000847222204131792, 2.70515273679747e-05,
    0.000326844162490547, 0.00135256413351957, 0.0022188465918784,
    0.00337126604512471, 0.00374620280538731, 0.00434814343158575,
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    0.000139109866047115, 0.000296579927149878, 0.000740568197543185,
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    ]).reshape(24, 24, order='F')

mpg_bs_poisson.hat = np.array([
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    0.0253300650870489, 0.0278009383597399, 0.0414535179805262,
    0.0409418131857217, 0.014157417875456, 0.0130282960809025,
    0.138598903485126, 0.0119217227225527, 0.0118879906828549,
    0.0141551657727111, 0.123783953258847, 0.11912545095407,
    0.0142268306190214, 0.0172399619813334, 0.121846395543782,
    0.110470500089909, 0.0201551990336182, 0.0206310745751254,
    0.0201536865625068, 0.0381417658252338, 0.147146341940793,
    0.19291538753225, 0.0138996856259478, 0.0145038473004087,
    0.0816344563835501, 0.0816344563835501, 0.0141422035219747,
    0.0148789474728842, 0.0154853081668733, 0.0606395671173926,
    0.0600386292911446, 0.0489884539186971, 0.0451161294598349,
    0.0286513771162332, 0.0288498072754457, 0.0281198522360051,
    0.0237262441315675, 0.0231072922711262, 0.0233676261568825,
    0.013768548515088, 0.0737736816925903, 0.0160520266152111,
    0.0160520266152111, 0.0178865660055972, 0.0246890354271123,
    0.0236757874745378, 0.0207029571112752, 0.0208006880192332,
    0.0871658869688338, 0.0121224793768177, 0.0871468068056535,
    0.012134436832306, 0.0130273070409246, 0.0770270710340484,
    0.0161554288190356, 0.0130060409476929, 0.0131560893519169,
    0.0233495425653485, 0.076546096962634, 0.026653503029336,
    0.0232595679594293, 0.025927102793454, 0.0236164317298477,
    0.0261567967057281, 0.0234612845321249, 0.0224086568560985,
    0.023932672432481, 0.0226000918010269, 0.0185252878980513,
    0.0596358457431316, 0.0267683181821813
    ])
mpg_bs_poisson.Ve = np.array([
    0.00872816910147373, -0.00339468396176873, -0.00574795519757804,
    -0.00574495699516135, 0.00224716621100421, 0.000990731701491968,
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    0.000118849290312744, 0.000936311632940437, 0.00169780728892799,
    0.00302963699787248, 0.00399780133211975, 0.00495376916948824,
    -0.00339468396176873, 0.00304577329137564, 0.00073225495104467,
    0.000711491834517779, -0.00160491657736677, -0.000740799055737486,
    6.70583600031394e-05, 0.000178733920236327, 0.000549296431166237,
    0.00056281173695013, 0.00095383610203774, 0.00175022827549142,
    0.00196954338218556, 0.00232358726439227, 0.00278825293492344,
    0.00117312403915761, 0.000540827007066709, 6.60591721757886e-05,
    -9.18905851433959e-05, -0.000701585089614169, -0.00126800242591629,
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    -0.00574795519757804, 0.000732254951044672, 0.00559376431953148,
    0.0048832815861983, -0.00121036529463201, -0.000522618964878643,
    0.000129331466175766, 0.000229756269964103, 0.000520627200771865,
    0.000528242761654376, 0.000793896658595473, 0.00131409709061904,
    0.00123306001882202, 0.00121204524436677, 0.00133977901167853,
    0.000447066868582399, 0.000206656393910154, 2.68120895197694e-05,
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    0.00794460693942518, 0.0152750741086272, 0.0213508475270903
    ]).reshape(24, 24, order='F')

mpg_bs_poisson.edf_all = np.array([
    1, 1, 1, 0.999999999999997, 0.138685243481343, 0.168020596767126,
    0.0480405022511898, 0.18786734224937, 0.263638943922787,
    0.146831418768435, 0.125821886761792, 0.418244018451644,
    0.291958034715267, 0.259042421165291, 0.491718306066333,
    0.0608764913352002, 0.0736205393644838, 0.0110857218278732,
    0.028133863911819, 0.110523411158202, 0.279890851829335,
    0.403214040630245, 0.283218020486634, 0.455736703811175
    ])
mpg_bs_poisson.edf1 = np.array([
    1, 1, 1, 1, 0.165131311402747, 0.216628750997608, 0.129858492449922,
    0.280058095563882, 0.341868441797401, 0.189284980057434,
    0.176944029164426, 0.479986234409329, 0.34393973921471,
    0.289638759873068, 0.596490313025557, 0.0984847261596164,
    0.0965664680726603, 0.0206746315637887, 0.0529176753037307,
    0.200932398735361, 0.350970375831462, 0.461975872101362,
    0.300898156007137, 0.569596548903937
    ])
mpg_bs_poisson.R = np.array([
    -71.5821206726932, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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    -2.78710370715732, 0.501821705771827, 0.199861513010874,
    0.0705449671051595, 0.65845009000007, 0.271741081238087,
    0.0296247577347264, 2.29204423094291, 0.295179535981046,
    -0.498021301042363, -0.191083976072719, 0.10022620819189,
    0.149938173134123, -0.000647738071340076, 0.0932506134932401,
    -0.255992650247112, -0.173965873880064, 0.0746464264713858,
    -0.0438028632249015, -0.161854602228138, -0.0578372113962488,
    0.157297649723395, 0.195127506637709, -0.126981325801707,
    -0.201542068281104, 0.633451731139413, 1.23686433543792,
    0.0741286829376448, 3.93651436085439, 0, 0, 0
    ]).reshape(24, 24, order='F')

mpg_bs_poisson.nsdf = 4
mpg_bs_poisson.sig2 = 1
mpg_bs_poisson.cmX = np.array([
    1, 0.901477832512315, 0.58128078817734, 0.374384236453202, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
    ])
mpg_bs_poisson.assign = np.array([
    0, 1, 2, 2
    ])
mpg_bs_poisson.offset = np.array([
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
    ])
mpg_bs_poisson.df_residual = 194.753831641044
mpg_bs_poisson.min_edf = 6
mpg_bs_poisson.optimizer = np.array([
 'outer', 'newton'
    ])
mpg_bs_poisson.p_coeff = np.array([
    3.38155196812657, -0.2398057983025, 0.0385574655971876,
    0.0308789097162079
    ])
mpg_bs_poisson.se = np.array([
    0.0953875221712041, 0.0567945319890742, 0.0752322831366413,
    0.0782324137428169, 0.0683836244631848, 0.0338714874552585,
    0.0343335878487408, 0.0327660488124372, 0.0419264132225423,
    0.036884405501987, 0.0465922758083723, 0.0708176444724415,
    0.0738292412039099, 0.0885015393163463, 0.116977237674529,
    0.0436765043288013, 0.0191231267976919, 0.0122089069890121,
    0.0159907519222515, 0.0342192166136885, 0.0515106100154504,
    0.0817203372793118, 0.116254024441009, 0.164262927556978
    ])
mpg_bs_poisson.p_t = np.array([
    35.4506741673956, -4.22233954403625, 0.512512235301397,
    0.394707362829427
    ])
mpg_bs_poisson.p_pv = np.array([
    2.83211381163377e-275, 2.41779512472801e-05, 0.608292559428079,
    0.693058868325634
    ])
mpg_bs_poisson.residual_df = 194.753831641044
mpg_bs_poisson.m = 2
mpg_bs_poisson.chi_sq = np.array([
    24.0890023854214, 12.9900133623474
    ])
mpg_bs_poisson.s_pv = np.array([
    4.50054787026542e-05, 0.0019804834204652
    ])
mpg_bs_poisson.scale = 1
mpg_bs_poisson.r_sq = 0.88083178283537
mpg_bs_poisson.n = 203
mpg_bs_poisson.dev_expl = 0.888606170071194
mpg_bs_poisson.edf = np.array([
    2.53986871460058, 1.70629964435497
    ])
mpg_bs_poisson.dispersion = 1
mpg_bs_poisson.pTerms_pv = np.array([
    2.41779512472801e-05, 0.874926047858207
    ])
mpg_bs_poisson.pTerms_chi_sq = np.array([
    17.8281512251323, 0.267231825859481
    ])
mpg_bs_poisson.pTerms_df = np.array([
    1, 2
    ])
mpg_bs_poisson.cov_unscaled = np.array([
    0.00909877938596195, -0.0036458463607326, -0.00588941030822462,
    -0.00591326964084843, 0.00270235043019556, 0.00119384910564329,
    -0.000286097332788069, -0.000538328684681065, -0.00118730774061063,
    -0.00118703780057675, -0.00172055345918883, -0.00282973019584382,
    -0.00279619972795585, -0.00289060805206229, -0.00320325258490701,
    -0.00198704716570621, -0.000847222204131792, 2.70515273679747e-05,
    0.000326844162490547, 0.00135256413351957, 0.0022188465918784,
    0.00337126604512471, 0.00374620280538731, 0.00434814343158575,
    -0.0036458463607326, 0.00322561886385797, 0.000818643340518427,
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    0.000139109866047115, 0.000296579927149878, 0.000740568197543185,
    0.000742005374071472, 0.00114977928877198, 0.00200530841336703,
    0.00222811101992064, 0.0024818042434364, 0.00282650455393769,
    0.00146020870965775, 0.000618887019138287, -2.43124025263358e-05,
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    0.000134212108893823, 0.000277278672318982, 0.000624575880555572,
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    0.000242162647776926, 4.77172748392219e-06, -0.00037150498020405,
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    0.000134563688210397, 0.000592929102356622, 0.000989340528530486,
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    0.0011787952545672, 0.00103397353567405, 0.00113707718574448,
    0.000747670650180816, 0.000637468318331369, 0.000963934771472604,
    0.000259239399462997, -3.13740933986805e-05, -9.8258454258689e-05,
    0.000164389952506784, 4.78756078126526e-05, -4.80106456474847e-05,
    -8.33829838965235e-05, -0.000166890570625039, -0.000205380711729347,
    -9.50907045530586e-05, 1.1901907320833e-05, 7.14426605544705e-05,
    -0.000538328684681065, 0.000296579927149878, 0.000277278672318982,
    0.000317400792863984, -0.0015159828401994, 4.77172748392219e-06,
    0.00103397353567405, 0.00107361395477902, 0.00125357393902288,
    0.000898511162091638, 0.000812608069611423, 0.00117847586239195,
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    0.00566170373323159, 0.0166147008644102, 0.0269823093695889
    ]).reshape(24, 24, order='F')

mpg_bs_poisson.cov_scaled = mpg_bs_poisson.cov_unscaled

mpg_bs_poisson.p_table = np.array([
    3.38155196812657, -0.2398057983025, 0.0385574655971876,
    0.0308789097162079, 0.0953875221712041, 0.0567945319890742,
    0.0752322831366413, 0.0782324137428169, 35.4506741673956,
    -4.22233954403625, 0.512512235301397, 0.394707362829427,
    2.83211381163377e-275, 2.41779512472801e-05, 0.608292559428079,
    0.693058868325634
    ]).reshape(4, 4, order='F')

mpg_bs_poisson.pTerms_table = np.array([
    1, 2, 17.8281512251323, 0.267231825859481, 2.41779512472801e-05,
    0.874926047858207
    ]).reshape(2, 3, order='F')

mpg_bs_poisson.s_table = np.array([
    2.53986871460058, 1.70629964435497, 3.20982914795608, 2.15301685267905,
    24.0890023854214, 12.9900133623474, 4.50054787026542e-05,
    0.0019804834204652
    ]).reshape(2, 4, order='F')

mpg_bs_poisson.method = 'UBRE'
mpg_bs_poisson.sp_criterion = -0.733688545240377
mpg_bs_poisson.rank = 24
mpg_bs_poisson.np = 24
mpg_bs_poisson.params = np.array([
    3.38155196812657, -0.2398057983025, 0.0385574655971876,
    0.0308789097162079, 0.322087207070371, 0.114222640046661,
    -0.0703319473834318, -0.0932993942273026, -0.164529819124519,
    -0.150860338924587, -0.19827414261489, -0.318132823476157,
    -0.277920321049033, -0.291752287584618, -0.335850782440607,
    0.147266917321495, 0.0655746640077953, 0.00329634588440575,
    -0.0178203244735351, -0.0949069908870388, -0.162736107333124,
    -0.267760412623755, -0.31426029441775, -0.36537967845964
    ])
